A Three-Layer Privacy Preserving Cloud Storage Scheme Based on Computational Intelligence in Fog Computing

Wang, Tian; Zhou, Jiyuan; Chen, Xinlei; Wang, Guojun; Liu, Anfeng; Liu, Yang

doi:10.1109/tetci.2017.2764109

Cited by 90 publications

(37 citation statements)

References 25 publications

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“…Edge computing is a promising paradigm to address these issues due to its unique features. There are some schemes that focus on cloud data storage by applying edge computing to fight against data theft attack [99] and insider attack [100], [101].…”

Section: Secure Data Storage In Edge Computingmentioning

confidence: 99%

“…To resist inside attack in a cloud server, Wang et al [100] designed a three-layer hierarchical storage scheme in fog computing, which divides user data into three parts of different size with a Hash-Solomon code algorithm and stores them in local device, fog node and cloud server, respectively. Allocating the different ratio of user data stored in different devices makes insider attackers impossible to recover the real user data even if they get all data in a certain device.…”

Section: Secure Data Storage In Edge Computingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

143

103

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Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications such as environmental monitoring, smart navigation and smart healthcare come with new requirements such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.

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Section: Secure Data Storage In Edge Computingmentioning

confidence: 99%

Section: Secure Data Storage In Edge Computingmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

143

103

View full text Add to dashboard Cite

show abstract

“…Lyu et al proposed a privacy-preservation system for fog-based aggregation in Smart Grid with differential privacy so as to enable the intermediate fog nodes to safely collect data from connected smart meters [17]. Wang et al presented a three-layer privacy preserving cloud storage scheme in cloud computing to resist possible attacks from the inside of cloud servers [18]. Xue et al designed verifiable security fine-grained access control in vehicular cloud computing to share latency-sensitive data [19].…”

Section: Researches On Relevant Privacy-protection Schemesmentioning

confidence: 99%

Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing

Xiao

Zheng

2020

Sensors

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Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure.

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“…The fog computing layer is deployed between cloud and IoT devices. This layer plays an important role in the transmission between the cloud computing layer and the device layer [12]. Fog layer contains network devices such as router, access points, gateways, switches, and video surveillance cameras.…”

Section: Fog Layermentioning

confidence: 99%

Fog Computing, Applications, Security and Challenges, Review

Rahman

Wen

2018

IJET

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The internet of things originates a world where on daily basis objects can join the internet and interchange information and in addition process, store, gather them from the nearby environment, and effectively mediate on it. A remarkable number of services might be imagined by abusing the internet of things. Fog computing which is otherwise called edge computing was introduced in 2012 as a considered is a prioritized choice for the internet of things applications. As fog computing extend services of cloud near to the edge of the network and make possible computations, communications, and storage services in proximity to the end user. Fog computing cannot only provide low latency, location awareness but also enhance real-time applications, quality of services, mobility, security and privacy in the internet of things applications scenarios. In this paper, we will summarize and overview fog computing model architecture, characteristic, similar paradigm and various applications in real-time scenarios such as smart grid, traffic control system and augmented reality. Finally, security challenges are presented.

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A Three-Layer Privacy Preserving Cloud Storage Scheme Based on Computational Intelligence in Fog Computing

Cited by 90 publications

References 25 publications

A Survey on Secure Data Analytics in Edge Computing

A Survey on Secure Data Analytics in Edge Computing

Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing

Fog Computing, Applications, Security and Challenges, Review

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